Phi-4 vs Qwen2.5-VL 72B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Microsoft 的 Phi-4 与 Alibaba 的 Qwen2.5-VL 72B 是当前备受关注的两款主力 AI 模型。Phi-4 提供 16.4K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Qwen2.5-VL 72B 提供 131.1K Token 上下文窗口，定价为每百万 Token 输入 $0.00 / 输出 $0.00。以下为两者的基准测试、延迟与成本全面对比。
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Phi-4
开源权重基础大模型Microsoft 推出的 Phi-4 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 16K 上下文窗口、135 Token/秒生成速度与 80/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
16.4K tokens
N/A
Qwen2.5-VL 72B
原生多模态视觉模型Alibaba 推出的 Qwen2.5-VL 72B 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型，具备 131K 上下文窗口、55 Token/秒生成速度与 79/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
131.1K tokens
38%
核心差异一览
- Both models cost the same under this workload — choose on capability, not price.
- Qwen2.5-VL 72B has a larger context window (131K vs 16K tokens).
- Phi-4 is faster at 135 tok/s vs 55 tok/s.
| Feature | Phi-4 | Qwen2.5-VL 72B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 79 |
Provider | Microsoft | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 16.4K tokens~25 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 135 tok/s | 55 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 38% |
GPQA Diamond Graduate-level reasoning | 55% | 58% |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Phi-4 · Strengths & Key Advantages
- 提供 16K 上下文窗口与最高 4K 输出容量，核心基准表现优异（GPQA Diamond 55% · MMLU-Pro 78.5%）
- 高达 135 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 270 毫秒
- 开放模型权重 (14B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Qwen2.5-VL 72B · Strengths & Key Advantages
- 提供 131K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 38% · GPQA Diamond 58%）
- 高达 55 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 720 毫秒
- 开放模型权重 (72B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Phi-4 | Qwen2.5-VL 72B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 12K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 3K | 6K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Phi-4 | Qwen2.5-VL 72B |
|---|---|---|
| Official API Model ID (SDK) | phi-4 | qwen2-5-vl-72b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-12 | 2024-09 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Synthetic-Data Dense Reasoning Transformer | Open-Weights Multimodal Vision-Language Transformer |
| Parameters | 14B | 72B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Phi-4 vs Qwen2.5-VL 72B: 常见问题解答 (FAQ)
Which is more intelligent, Phi-4 or Qwen2.5-VL 72B?
Phi-4 is more intelligent. It scores 80 compared with 79 on the composite Intelligence Index.
Which model is faster?
Phi-4 is faster, generating 135 tokens per second compared with 55 tokens per second.
Which model has lower latency?
Phi-4 has lower latency, with a time to first token of 0.27s compared with 0.72s.
Which model has a larger context window?
Qwen2.5-VL 72B has a larger context window, supporting 131.1K tokens compared with 16.4K tokens.
综合结论
综合来看，Phi-4（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Qwen2.5-VL 72B（适合：原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Phi-4 在综合智能与编程基准测试中表现更强，Phi-4 拥有更快的 Token 生成速度，而 Phi-4 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Phi-4 和 Qwen2.5-VL 72B 哪个更强？+
在综合智能指数与编程基准评测中，Phi-4 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Phi-4 与 Qwen2.5-VL 72B 的 API 价格哪个更便宜？+
Phi-4 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Qwen2.5-VL 72B 为输入 $0.00 / 输出 $0.00。综合混合成本来看，Phi-4 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Phi-4 支持 16.4K Token 上下文窗口，而 Qwen2.5-VL 72B 支持 131.1K Token。在流式生成速度方面，Phi-4 的吞吐表现更快。
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: Microsoft 与 Alibaba 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。